Simplest volatility bandsVolatility bands based on average candle percentage spread. Tested on BTCUSD charts only.
Based on the 68-95-99.7 rule, it seems that the spread, for daily and 4-H candles, follows a normal distribution: that means, around 85% of candles have a %-spread within sma(low/high, some_len) and sma(high/low, some_len) , and around 95% of candles within the pow2 of that range.
If you take the mean between the boundaries of the first %-spreads band, and calculate the 1.5 standard deviation of past some_len candles (I'm speaking from memory, it has been a while since I did them), the 1.5 standard deviation bands match similarly the %-spread bands, and around 85% of the candles are within these %-spread bands.
If you then take the pow2 of the bands, it will be similar to the 2 * std of the original bands, with around 95% of data within the pow2 bands.
You can take ema or other similar means with similar results, and the same for different lengths, but it seems that sma with a len of 14 is the more stable ones for both daily and 4-H, and taken other average calculations doesn't cause too many differences respect to the sma. I haven't tested too much for lower or higher timeframes.
With those %-spread bands, I multiple and divide those spreads to the open value of a new candle to get the two bands.
So, in short, you know that 85% of candles are within the closer bands, and around 95% of candles, around the bigger one. Once a new candle is born, the bands won't move (the bands are calculated from the previous candle, so the current candle's price movement doesn't move the band).
Going out the bands implies a sudden increase in volality, which usually causes rejection. They happen mostly at breakouts and ends of heavy trends. If a candle closes above the bigger band, you have probably got a breakout (a rejection rarely happens if the candle have already closed), although a breakout can happen without closing above the bands if volatility was already high.
If a trend is already stablished and is healthy, you won't probably see candles going out the bands, not even with a wick. When the trend is parabolic, and goes above the candle, the trend has probably ended, although the trend can be exhausted without going out the bands as well.
Heavy but not yet exhausted trends (specially recently started heavy downtrends), usually reach the bottom of the bigger bands during 4 o 5 contiguous candles (check visually looking at bitcoin history though, I'm speaking from memory).
So, the possibilities are multiple and you cannot use the bands to form a strategy, as usual. It can be comfortable enough psycologically for going to sleep, by moving your stop-loss to a point out of the bands in the opposite direction of your trade, and adjusting your position size accordingly; or just to check momentum looking at how close are the candle limits to the bands.
But, as usual, you are responsible of what you do with your money :)
In den Scripts nach "Volatility" suchen
Volatility Backtest The Volatility function measures the market volatility by plotting a
smoothed average of the True Range. It returns an average of the TrueRange
over a specific number of bars, giving higher weight to the TrueRange of
the most recent bar.
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
Volatility Strategy The Volatility function measures the market volatility by plotting a
smoothed average of the True Range. It returns an average of the TrueRange
over a specific number of bars, giving higher weight to the TrueRange of
the most recent bar.
WARNING:
- This script to change bars colors.
Volatility Calculator for Daily Top and Bottom RangeWith the usage of ATR, applied on the close of the daily candle, I am calculated the volatility channels for the TOP and BOTTOM
Based on this logic, we can estimate, with a huge confidence factor, where the prices are going to be compressed for the trading day.
Having said that, lets take a look at the data gathered among the most important financial markets:
SPX
TOP CROSSES : 2116
BOT CROSSES : 1954
Total Daily Candles : 18908
Occurance ratio = 0.215
NDX
TOP CROSSES : 1212
BOT CROSSES : 1183
Total Daily Candles : 9386
Occurance ratio = 0.255
DIA
TOP CROSSES : 759
BOT CROSSES : 769
Total Daily Candles : 6109
Occurance ratio = 0.25
DXY
TOP CROSSES : 1597
BOT CROSSES : 1598
Total Daily Candles : 13156
Occurance ratio = 0.243
DAX
TOP CROSSES : 1878
BOT CROSSES : 1848
Total Daily Candles : 13155
Occurance ratio = 0.283
BTC USD
TOP CROSSES : 416
BOT CROSSES : 417
Total Daily Candles : 4290
Occurance ratio = 0.194
ETH USD
TOP CROSSES : 247
BOT CROSSES : 268
Total Daily Candles : 2452
Occurance ratio = 0.21
EUR USD
TOP CROSSES : 820
BOT CROSSES : 805
Total Daily Candles : 7489
Occurance ratio = 0.217
GOLD
TOP CROSSES : 1722
BOT CROSSES : 1569
Total Daily Candles : 13747
Occurance ratio = 0.239
USOIL
TOP CROSSES : 1077
BOT CROSSES : 1089
Total Daily Candles : 10231
Occurance ratio = 0.212
US 10Y
TOP CROSSES : 1302
BOT CROSSES : 1365
Total Daily Candles : 9075
Occurance ratio = 0.294
Based on this, we can assume with a very high confidence ( 70-80%) that the market is going to stay, within the range created from the BOT and TOP ATR points.
Volatility Adapted Relative StrengthVARS uses a stock's ALPHA in comparison to the SPX to determine whether there is RS on an volatility adjusted basis.
Volatility Breakout StrategyThis is a strategy used by Larry R. Williams called Volatility Breakout.
By identifying a strong uptrend that exceeds 'a certain level' on a daily basis as a breakout signal, enter long position, take advantage of long at the the next day's open.
'a certain level (Entry Price)' is calculated by { close + 'k' * high -low }, and applied logarithmic calculation.
Stop loss level is calculated by half of the previous day's Low and Entry Price.
Strategy exit always at UTC+0.
And expressed day session with the background color.
Thanks and hope this helps you.
(kor)
이건 Larry R. Williams가 사용하는 변동성 돌파 전략입니다.
특정 수준을 넘는 강한 상승세를 돌파 신호로 파악하여 매수하고, 장시간이 종료될때에 빠르게 수익을 실현하는 전략입니다.
"특정 레벨(진입가)"은 close + 'k' * high -low 로 계산되며 로그 계산을 적용했습니다.
손절 레벨은 전일 저가 및 진입 가격의 절반으로 계산됩니다.
전략은 항상 UTC+0에서 종료합니다. ( 한국 기준 오전 9시 )
그리고 배경색으로 일일세션을 표현했습니다.
트뷰에 오픈소스의 래리윌리엄스 변동성전략이 없는것 같아 간단하게나마 만들어 보았습니다. 도움이 되었으면 좋겟워요..
Volatility Quality Histogram (NicoadW)This indicator is based on the Volatility Quality Index ( VQI ) by Thomas Stridsman.
It shows the slope of the VQI in form of a histogram.
The VQI is calculated in the following Steps:
1. Applying a WeightedMovingAverage with the onto the low, open, high, close and prior close
2. Calculating the true range and the range from the results of step 1 and setting them into relation
3. Calculating a weighted lumpsum of the results from step 2 (This is the value of the widely known VQI )
4. The change from the current value of step 3 compared to the last value is the VQI-Slope
5. The VQI-Slope is filtered by only recogning changes greater as the User-Input
6. If the Slope is rising, its considered a long trend and if its falling its considered a short trend
User Inputs
Valuation Period: Length of the WMAs
Filter in Pips: minimum change of the VQI-Slope to result in a Trend-Change
Visuals
Inrease the size of the Signal: Highligts the Trend-Changes
Color Chart Bars: Colors the bars of the main chart depending on the trend given by the VQI
Volatility OscillatorThis tool displays relative volatility and directional trend. Excellent way to pickup diversions and reversals. Length can be lowered to 11 or 13 in settings to show price range.
Can be used to identify patterns such as parallel channels and likely direction of price action as pictured below.
Volatility Support & Resistance [LM]Hello guys,
I would like to introduce you volatility support and resistance horizontals. It draws line horizontal on high and low vhighly volatile candles. You can select lines on this timeframe and/or another one. It's done in the same spirit like my other indicators for horizontals
The colors are dynamic depending whether price is below or above the line:
for the current timeframe green is used for support and blue for resistance
for the other timeframe by default 4h orange is used for support and violet for resistance
There are various sections in setting:
general settings - here you can select ATR length, multiplier and show labels
show and hide section of the current timeframe
other timeframe
show and hide other timeframe horizontals
Any suggestions are welcome
Volatility Adjusted Bands - JD This indicator gives a likely trading range based on the volatility of the past x amount of bars, measured against a certain moving average.
The indicator can be used as an alternative for BBands.
It gives approx. the same "trend-side" lines (upper line in uptrend, lower line in down trend) as the Bollinger Bands, but the opposite line follows closer on price,
Instead of "flying out" to the other direction like in BBands.
As a comparison, the BBands for the same length (50 period) are added on the chart.
JD.
#NotTradingAdvice #DYOR
I build these indicators for myself and provide them open source, to use for free to use and improve upon,
as I believe the best way to learn is toghether.
Volatility Direction DetectorThis script helps trader identify volatility direction in order to classify each market move. It also provides good entry points.
Volatility RatioVolatility Ratio indicator script. This indicator was originally developed by Jack Schwager.
Volatility Finite Volume Elements Backtest This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
Volatility Finite Volume Elements Strategy This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
WARNING:
This script to change bars colors.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
Examples:
Shannon Entropy Volatility AnalyzerThis algorithm aims to measure market uncertainty or volatility using a Shannon entropy-based approach. 🔄📊
Entropy is a measure of disorder or unpredictability, and here we use it to evaluate the structure of price returns within a defined range of periods (window length). 🧩⏳ Thus, the goal is to detect changes to identify conditions of high or low volatility. 🔍⚡
What we seek with Shannon's formula in this algorithm is to measure market uncertainty or volatility through dynamic entropy. This measure helps us understand how unpredictable price behavior is over a given period, which is key to making informed decisions. 📈🧠
Through this formula, we calculate the level of disorder or dispersion in price returns based on their probability of occurrence, enabling us to identify moments of high or low volatility. 💡💥
Shannon Entropy Calculation 📏
• Uses probabilities to measure uncertainty in returns. 🎲
• Entropy is normalized on a scale of 0 to 100, where:
o High Entropy: Unpredictable movements (high uncertainty). ⚠️💥
•
o Low Entropy: Structured movements (low uncertainty). 📉🔒
•
• With probabilities, we measure the level of dispersion or unpredictability of returns using Shannon's entropy formula. 📊🔍
________________________________________
Indicator Usefulness 🛠️
• Identify High Volatility: When the market is unpredictable, the indicator signals "High Uncertainty." ⚡🔮
• Detect Market Stability: When the market is more predictable and structured, the indicator highlights "Low Uncertainty." 🔒🧘♂️
• Neutral Zones: Helps monitor markets without extreme conditions, enabling safer entry or exit opportunities. ⚖️🚶♂️
________________________________________
Uncertainty Zones 🌀
1. High Uncertainty: When entropy exceeds the upper threshold. 🚨🔺
2. Low Uncertainty: When entropy is below the lower threshold. 🔻💡
3. Neutral: When entropy lies between both thresholds. ⚖️🔄
________________________________________
What We Aim to Achieve with the Formula in Practice 🎯
1. Detection of Volatile Moments: Shannon’s formula helps us identify when the market is unpredictable. This is a good moment to take additional precautions, such as reducing position size or avoiding trading during high volatility phases. ⚠️📉
2. Trading Opportunities in Stable Markets: With low entropy, we can identify when the market is more predictable, favoring trend or momentum strategies with a higher chance of success. 🚀📈
3. Optimization of Risk Management: By measuring market volatility in real-time, we can adjust entry and exit strategies, tailoring risk based on the level of uncertainty detected. 🔄⚖️
________________________________________
We hope this makes it easy to interpret and use. If you have any questions or comments, please feel free to reach out to us! 📬😊
Logarithmic VolatilityIntroducing the Logarithmic Volatility Indicator , an innovative trading indicator designed especially for trading in low volatility markets. This powerful indicator is aimed at traders of all levels, from beginners to experts, and is based on fundamental concepts of mathematics and statistics applied to the financial market. Its main objective is to provide you with a better understanding of price movements and help you make more accurate investment decisions, especially in low volatility environments.
The purpose of this indicator is to find a volatility estimator that depends on the difference between High and Low, taking into account that this measure is directly proportional to volatility. A first result was obtained by Parkinson (1980) which was later improved by Garman and Klass (1980), who improved the estimator by obtaining one of minimum variance. It is the simplified version (and recommended by them) of the Garman and Klass estimator that is used to calculate the daily volatility of the asset.
The Logarithmic Volatility Indicator is a unique smoothing indicator that uses logarithms and volatility calculation of the opening, high, low and closing prices. It combines these elements to obtain an accurate representation of market volatility in situations where volatility is low.
Features
This indicator has several outstanding features designed to enhance your trading analysis in low volatility environments:
• Intraday Volatility Calculation: This innovative feature allows you to view market volatility levels in real time, providing a clear view of market fluctuations even when volatility is low.
• EMA (Exponential Moving Average) Multi Length: The indicator incorporates three different EMA lengths (Fast, Medium and Slow). This gives you a deeper and more detailed analysis of market volatility, allowing you to detect subtle changes in volatility and make more accurate predictions.
• Visual color change: The indicator uses a color change between green and red to facilitate quick interpretation of the market. Green indicates a decrease in volatility, while red indicates an increase in volatility. This feature helps you quickly identify changes in market dynamics even in periods of low volatility.
• Histogram display: In addition to the colors, the indicator can also be displayed as a histogram. This intuitive representation allows you to visually observe changes in volatility over time and detect emerging patterns or trends in markets with low volatility.
Settings
The Logarithmic Volatility Indicator allows you to customize various settings to suit your specific trading needs:
• Slow EMA length: you can select the length of the slow exponential moving average according to your preferences and trading strategies.
• Fast EMA length: Similarly, you can choose the length of the fast exponential moving average to suit your trading style.
• Average EMA length: In addition to the two EMA lengths above, this indicator offers a third EMA length for even more detailed analysis. This additional feature is especially useful when trading in markets with low volatility, as it allows you to capture subtle changes in market dynamics.
Trading
The Logarithmic Volatility Indicator is designed not only to provide you with essential information about market volatility, but also to give you clear indications on when to trade. Here's how you can use the indicator's colors to guide your trading decisions:
- Long Trading: When the fast EMA has a smaller value than the slow EMA, the indicator will change to green. This is a signal to enter a long trade. That is, you can consider buying at this point, as an increase in price is anticipated due to decreasing volatility. With volatility declining, there is a greater likelihood that the price will continue in the current direction rather than fluctuate erratically.
- b]Short Trading: On the other hand, when the fast EMA has a higher value than the slow EMA, the indicator will turn red. This is a signal to enter a short trade. In other words, you may consider selling at this point, as a decline in price is anticipated due to rising volatility. With volatility on the rise, there is a greater risk of steeper price fluctuations.
It is important to remember that, as with any indicator, the Logarithmic Volatility Indicator does not guarantee 100% success. You should always use this indicator in combination with other analytical tools and good risk management. This tool provides you with an overview of market volatility and can help you identify trading opportunities in low volatility markets, but the final decision on when and how to trade should always be based on your own analysis and judgment.
In conclusion, the Logarithmic Volatility Indicator is an essential trading tool that every trader should have in their arsenal, especially when facing low volatility markets. With its accurate volatility calculation and easy-to-understand visualization, it will help you improve your trading decisions and maximize your profits even in situations where price movements are less pronounced. Try it today and take advantage of its efficiency in low volatility environments!
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Presentamos el Indicador de Volatilidad Logarítmica , un innovador indicador de trading diseñado especialmente para operar en mercados con baja volatilidad. Este poderoso indicador está dirigido a traders de todos los niveles, desde principiantes hasta expertos, y se basa en conceptos fundamentales de matemáticas y estadísticas aplicadas al mercado financiero. Su objetivo principal es proporcionarte una mejor comprensión de los movimientos de precios y ayudarte a tomar decisiones de inversión más precisas, especialmente en entornos de baja volatilidad.
Con este indicador se pretende encontrar un estimador de la volatilidad que dependa de la diferencia entre el High y el Low, teniendo en cuenta que esta medida es directamente proporcional a la volatilidad. Un primer resultado fue obtenido por Parkinson (1980) que posteriormente fue mejorado por Garman y Klass (1980), que mejoraron el estimador obteniendo uno de varianza mínima. Es la versión simplificada (y recomendada por ellos mismos) del estimador de Garman y Klass la que se utiliza para calcular la volatilidad diaria del activo.
El Indicador de Volatilidad Logarítmica es un indicador de suavizado único que utiliza logaritmos y el cálculo de la volatilidad de los precios de apertura, máximo, mínimo y cierre. Combina estos elementos para obtener una representación precisa de la volatilidad del mercado en situaciones donde la volatilidad es baja.
Características
Este indicador cuenta con varias características sobresalientes diseñadas para mejorar tu análisis de trading en entornos de baja volatilidad:
• Cálculo de la volatilidad intradía: Esta función innovadora te permite ver los niveles de volatilidad del mercado en tiempo real, lo que brinda una visión clara de las fluctuaciones del mercado incluso cuando la volatilidad es baja.
• EMA (Exponential Moving Average) Multi Longitud: El indicador incorpora tres longitudes diferentes de EMA (Rápida, Media y Lenta). Esto te proporciona un análisis más profundo y detallado de la volatilidad del mercado, permitiéndote detectar cambios sutiles en la volatilidad y realizar predicciones más precisas.
• Cambio de color visual: El indicador utiliza un cambio de color entre verde y rojo para facilitar la interpretación rápida del mercado. El verde indica una disminución de la volatilidad, mientras que el rojo indica un aumento de la volatilidad. Esta característica te ayuda a identificar rápidamente cambios en la dinámica del mercado incluso en períodos de baja volatilidad.
• Visualización Histograma: Además de los colores, el indicador también se puede visualizar como un histograma. Esta representación intuitiva te permite observar de manera visual los cambios en la volatilidad a lo largo del tiempo y detectar patrones o tendencias emergentes en mercados con baja volatilidad.
Ajustes
El Indicador de Volatilidad Logarítmica te permite personalizar varios ajustes para adaptarlos a tus necesidades de trading específicas:
• Longitud de EMA lenta: Puedes seleccionar la longitud de la media móvil exponencial lenta según tus preferencias y estrategias de trading.
• Longitud de EMA rápida: De manera similar, puedes elegir la longitud de la media móvil exponencial rápida para ajustarla a tu estilo de trading.
• Longitud de EMA media: Además de las dos longitudes de EMA anteriores, este indicador ofrece una tercera longitud de EMA para un análisis aún más detallado. Esta característica adicional es especialmente útil cuando operas en mercados con baja volatilidad, ya que te permite capturar cambios sutiles en la dinámica del mercado.
Operativa
El Indicador de Volatilidad Logarítmica está diseñado no solo para brindarte información esencial sobre la volatilidad del mercado, sino también para ofrecerte indicaciones claras sobre cuándo operar. Aquí te explicamos cómo puedes utilizar los colores del indicador para guiar tus decisiones de trading:
• Operativa en Largo: Cuando la EMA rápida tiene un valor más pequeño que la EMA lenta, el indicador cambiará a color verde. Esta es una señal para entrar en una operación en largo. Es decir, puedes considerar comprar en este punto, ya que se anticipa un aumento en el precio debido a la disminución de la volatilidad. Con la volatilidad en descenso, existe una mayor probabilidad de que el precio continúe en la dirección actual en lugar de fluctuar erráticamente.
• Operativa en Corto: Por otro lado, cuando la EMA rápida tiene un valor mayor que la EMA lenta, el indicador se tornará rojo. Esta es una señal para entrar en una operación en corto. En otras palabras, puedes considerar vender en este punto, ya que se anticipa una disminución en el precio debido al aumento de la volatilidad. Con la volatilidad en ascenso, existe un mayor riesgo de fluctuaciones de precio más pronunciadas.
Es importante recordar que, como con cualquier indicador, el Indicador de Volatilidad Logarítmica no garantiza un éxito del 100%. Siempre debes usar este indicador en combinación con otras herramientas de análisis y una buena gestión de riesgos. Esta herramienta te proporciona una visión general de la volatilidad del mercado y puede ayudarte a identificar oportunidades de trading en mercados con baja volatilidad, pero la decisión final de cuándo y cómo operar siempre deberá basarse en tu propio análisis y juicio.
En conclusión, el Indicador de Volatilidad Logarítmica es una herramienta de trading esencial que todo trader debe tener en su arsenal, especialmente cuando se enfrenta a mercados con baja volatilidad. Con su cálculo preciso de la volatilidad y su visualización fácil de entender, te ayudará a mejorar tus decisiones de trading y a maximizar tus ganancias incluso en situaciones donde los movimientos de precios son menos pronunciados. ¡Pruébalo hoy mismo y aprovecha su eficiencia en entornos de baja volatilidad!
BONK 1H Long Volatility StrategyGrok 1hr bonk strategy:
Key Changes and Why They’re Made
1. Indicator Adjustments
Moving Averages:
Fast MA: Changed to 5 periods (from, e.g., 9 on a higher timeframe).
Slow MA: Changed to 13 periods (from, e.g., 21).
Why: Shorter periods make the moving averages more sensitive to quick price changes on the 1-hour chart, helping identify trends faster.
ATR (Average True Range):
Length: Set to 10 periods (down from, e.g., 14).
Multiplier: Reduced to 1.5 (from, e.g., 2.0).
Why: A shorter ATR length tracks recent volatility better, and a lower multiplier lets the strategy catch smaller price swings, which are more common hourly.
RSI:
Kept at 14 periods with an overbought level of 70.
Why: RSI stays the same to filter out overbought conditions, maintaining consistency with the original strategy.
2. Entry Conditions
Trend: Requires the fast MA to be above the slow MA, ensuring a bullish direction.
Volatility: The candle’s range (high - low) must exceed 1.5 times the ATR, confirming a significant move.
Momentum: RSI must be below 70, avoiding entries at potential peaks.
Price: The close must be above the fast MA, signaling a pullback or trend continuation.
Why: These conditions are tightened to capture frequent volatility spikes while filtering out noise, which is more prevalent on a 1-hour chart.
3. Exit Strategy
Profit Target: Default is 5% (adjustable from 3-7%).
Stop-Loss: Default is 3% (adjustable from 1-5%).
Why: These levels remain conservative to lock in gains quickly and limit losses, suitable for the faster pace of a 1-hour timeframe.
4. Risk Management
The strategy may trigger more trades on a 1-hour chart. To avoid overtrading:
The ATR filter ensures only volatile moves are traded.
Trading fees (e.g., 0.5% on Coinbase) reduce the net profit to ~4% on winners and -3.5% on losers, requiring a win rate above 47% for profitability.
Suggestion: Risk only 1-2% of your capital per trade to manage exposure.
5. Visuals and Alerts
Plots: Blue fast MA, red slow MA, and green triangles for buy signals.
Alerts: Trigger when an entry condition is met, so you don’t need to watch the chart constantly.
How to Use the Strategy
Setup:
Load TradingView, select BONK/USD on the 1-hour chart (Coinbase pair).
Paste the script into the Pine Editor and add it to your chart.
Customize:
Adjust the profit target (e.g., 5%) and stop-loss (e.g., 3%) to your preference.
Tweak ATR or MA lengths if BONK’s volatility shifts.
Trade:
Look for green triangle signals and confirm with market context (e.g., volume or news).
Enter trades manually or via TradingView’s broker tools if supported.
Exit when the profit target or stop-loss is hit.
Test:
Use TradingView’s Strategy Tester to backtest on historical data and refine settings.
Benefits of the 1-Hour Timeframe
Faster Opportunities: Captures shorter-term uptrends in BONK’s volatile price action.
Responsive: Adjusted indicators react quickly to hourly changes.
Conservative: Maintains the 3-7% profit goal with tight risk control.
Potential Challenges
Noise: The 1-hour chart has more false signals. The ATR and MA filters help, but caution is needed.
Fees: Frequent trading increases costs, so ensure each trade’s potential justifies the expense.
Volatility: BONK can move unpredictably—monitor broader market trends or Solana ecosystem news.
Final Thoughts
Switching to a 1-hour timeframe makes the strategy more active, targeting shorter volatility spikes while keeping profits conservative at 3-7%. The adjusted indicators and conditions balance responsiveness with reliability. Backtest it on TradingView to confirm it suits BONK’s behavior, and always use proper risk management, as meme coins are highly speculative.
Disclaimer: This is for educational purposes, not financial advice. Cryptocurrency trading, especially with assets like BONK, is risky. Test thoroughly and trade responsibly.
SV Volatility Indicator BasicThe SV Volatility Indicator Basic in TradingView calculates and visualizes daily and average volatility over specified periods using three lines. Here’s what it does:
1. Daily Volatility Calculation. The indicator computes daily volatility as the percentage difference between the high and low prices relative to the closing price:
2. 30-day Moving Average of Volatility. A simple moving average (SMA) is applied to the daily volatility values over the last 30 days to smooth short-term fluctuations.
3. 90-day Moving Average of Volatility. Similarly, an SMA is calculated over the last 90 days to provide a longer-term view of volatility trends.
4. Visualization:
Three lines are plotted:
Red line: Represents the daily volatility in percentage terms.
Blue line: Displays the 30-day moving average of volatility.
Green line: Shows the 90-day moving average of volatility.
This indicator helps traders analyze market volatility by providing both immediate (daily) and smoothed (30-day and 90-day) measures, aiding in trend identification and risk assessment.
Uptrick: Crypto Volatility Index** Crypto Volatility Index(VIX) **
Overview
The Crypto Volatility Index (VIX) is a specialized technical indicator designed to measure the volatility of cryptocurrency prices. Leveraging advanced statistical methods, including logarithmic returns and variance, the Crypto VIX offers a refined measure of market fluctuations. This approach makes it particularly useful for traders in the highly volatile cryptocurrency market, providing insights that traditional volatility indicators may not capture as effectively.
Purpose
The Crypto VIX aims to deliver a nuanced understanding of market volatility, tailored specifically for the cryptocurrency space. Unlike other volatility measures, the Crypto VIX employs sophisticated statistical methods to reflect the unique characteristics of cryptocurrency price movements. This makes it especially valuable for cryptocurrency traders, helping them navigate the inherent volatility of digital assets and manage their trading strategies and risk exposure more effectively.
Calculation
1. Indicator Declaration
The Crypto VIX is plotted in a separate pane below the main price chart for clarity:
indicator("Crypto Volatility Index (VIX)", overlay=false, shorttitle="Crypto VIX")
2. Input Parameters
Users can adjust the period length for volatility calculations:
length = input.int(14, title="Period Length")
3. Calculating Daily Returns
The daily returns are calculated using logarithmic returns:
returns = math.log(close / close )
- **Logarithmic Returns:** These returns provide a normalized measure of price changes, making it easier to compare returns over different periods and across different assets.
4. Average Return Calculation
The average return over the specified period is computed with a Simple Moving Average (SMA):
avg_return = ta.sma(returns, length)
5. Variance Calculation
Variance measures the dispersion of returns from the average:
variance = ta.sma(math.pow(returns - avg_return, 2), length)
- Variance : This tells us how much the returns deviate from the average, giving insight into how volatile the market is.
6. Standard Deviation (Volatility) Calculation
Volatility is derived as the square root of the variance:
volatility = math.sqrt(variance)
- Standard Deviation : This provides a direct measure of volatility, showing how much the price typically deviates from the mean return.
7. Plotting the Indicator
The volatility and average return are plotted:
plot(volatility, color=#21f34b, title="Volatility Index")
plot(avg_return, color=color.new(color.red, 80), title="Average Return", style=plot.style_columns)
Practical Examples
1. High Volatility Scenario
** Example :** During significant market events, such as major regulatory announcements or geopolitical developments, the Crypto VIX tends to rise sharply. For instance, if the Crypto VIX moves from a baseline level of 0.2 to 0.8, it indicates heightened market volatility. Traders might see this as a signal to adjust their strategies, such as reducing position sizes or setting tighter stop-loss levels to manage increased risk.
2. Low Volatility Scenario
** Example :** In a stable market, where prices fluctuate within a narrow range, the Crypto VIX will show lower values. For example, a drop in the Crypto VIX from 0.4 to 0.2 suggests lower volatility and stable market conditions. Traders might use this information to consider longer-term trades or take advantage of potential consolidation patterns.
Best Practices
1. Combining Indicators
- Moving Averages : Use the Crypto VIX with moving averages to identify trends and potential reversal points.
- Relative Strength Index (RSI): Combine with RSI to assess overbought or oversold conditions for better entry and exit points.
- Bollinger Bands : Pair with Bollinger Bands to understand volatility relative to price movements and spot potential breakouts.
2. Adjusting Parameters
- Short-Term Trading : Use a shorter period length (e.g., 7 days) to capture rapid volatility changes suitable for day trading.
- Long-Term Investing : A longer period length (e.g., 30 days) provides a smoother view of volatility, helping long-term investors navigate market trends.
Backtesting and Performance Insights
While specific backtesting data for the Crypto VIX is not yet available, the indicator is built on established principles of volatility measurement, such as logarithmic returns and standard deviation. These methods are well-regarded in financial analysis for accurately reflecting market volatility. The Crypto VIX is designed to offer insights similar to other effective volatility indicators, tailored specifically for the cryptocurrency markets. Its adaptation to digital assets and ability to provide precise volatility measures underscore its practical value for traders.
Originality and Uniqueness
The Crypto Volatility Index (VIX) distinguishes itself through its specialized approach to measuring volatility in the cryptocurrency markets. While the concepts of logarithmic returns and standard deviation are not new, the Crypto VIX integrates these methods into a unique framework designed specifically for digital assets.
- Tailored Methodology : Unlike generic volatility indicators, the Crypto VIX is adapted to the unique characteristics of cryptocurrencies, providing a more precise measure of price fluctuations that reflects the inherent volatility of digital markets.
- Enhanced Insights : By focusing on cryptocurrency-specific price behavior and incorporating advanced statistical techniques, the Crypto VIX offers insights that traditional volatility indicators might miss. This makes it a valuable tool for traders navigating the complex and fast-moving cryptocurrency landscape.
- Innovative Application : The Crypto VIX combines established financial metrics in a novel way, offering a fresh perspective on market volatility and contributing to more effective risk management and trading strategies in the cryptocurrency space.
Summary
The Crypto Volatility Index (VIX) is a specialized tool for measuring cryptocurrency market volatility. By utilizing advanced statistical methods such as logarithmic returns and standard deviation, it provides a detailed measure of price fluctuations. While not entirely original in its use of these methods, the Crypto VIX stands out through its tailored application to the unique characteristics of the cryptocurrency market. Traders can use the Crypto VIX to gauge market risk, adjust their strategies, and make informed trading decisions, supported by practical examples, best practices, and clear visual aids.
EGARCH Volatility Estimator
EGARCH Volatility Estimator (EVE)
Overview:
The EGARCH Volatility Estimator (EVE) is a Pine Script indicator designed to quantify market volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. This model captures both symmetric and asymmetric volatility dynamics and provides a robust tool for analyzing market risk and trends.
Key Features:
Core EGARCH Formula:
ln(σ t 2 )=ω+α(∣ϵ t−1 ∣+γ⋅ϵ t−1 )+β⋅ln(σ t−1 2 )
ω (Omega): Captures long-term baseline volatility.
α (Alpha): Measures sensitivity to recent shocks.
γ (Gamma): Incorporates asymmetric effects (e.g., higher volatility during market drops).
β (Beta): Reflects the persistence of historical volatility.
The formula computes log-volatility, which is then converted to actual volatility for interpretation.
Standardized Returns:
The script calculates daily log-returns and standardizes them to measure deviations from expected price changes.
Percentile-Based Volatility Analysis:
Tracks the percentile rank of current volatility over a historical lookback period.
Highlights high, medium, or low volatility zones using dynamic background colors.
Dynamic Normalization:
Maps volatility into a normalized range ( ) for better visual interpretation.
Uses color gradients (green to red) to reflect changing volatility levels.
SMA Integration:
Adds a Simple Moving Average (SMA) of either EGARCH volatility or its percentile for trend analysis.
Interactive Display:
Displays current volatility and its percentile rank in a table for quick reference.
Includes high (75%) and low (25%) volatility threshold lines for actionable insights.
Applications:
Market Risk Assessment: Evaluate current and historical volatility to assess market risk levels.
Quantitative Strategy Development: Incorporate volatility dynamics into trading strategies, particularly for options or risk-managed portfolios.
Trend and Momentum Analysis: Use normalized or smoothed volatility trends to identify potential reversals or breakouts.
Asymmetric Volatility Detection: Highlight periods where downside or upside volatility dominates.
Visualization Enhancements:
Dynamic colors and thresholds make it intuitive to interpret market conditions.
Percentile views provide relative volatility context for historical comparison.
This indicator is a versatile tool for traders and analysts seeking deeper insights into market behavior, particularly in volatility-driven trading strategies.
Historical VolatilityHistorical Volatility Indicator with Custom Trading Sessions
Overview
This indicator calculates **annualized Historical Volatility (HV)** using logarithmic returns and standard deviation. Unlike standard HV indicators, this version allows you to **customize trading sessions and holidays** for different markets, ensuring accurate volatility calculations for options pricing and risk management.
Key Features
✅ Custom Trading Sessions - Define multiple trading sessions per day with precise start/end times
✅ Multiple Markets Support - Pre-configured for US, Russian, European, and crypto markets
✅ Clearing Periods Handling - Account for intraday clearing breaks
✅ Flexible Calendar - Set trading days per year for different countries
✅ All Timeframes - Works correctly on intraday, daily, weekly, and monthly charts
✅ Info Table - Optional display showing calculation parameters
How It Works
The indicator uses the classical volatility formula:
σ_annual = σ_period × √(periods per year)
Where:
- σ_period = Standard deviation of logarithmic returns over the specified period
- Periods per year = Calculated based on actual trading time (not calendar time)
Calculation Method
1. Computes log returns: ln(close / close )
2. Calculates standard deviation over the lookback period
3. Annualizes using the square root rule with accurate period count
4. Displays as percentage
Settings
Calculation
- Period (default: 10) - Lookback period for volatility calculation
Trading Schedule
- Trading Days Per Year (default: 252) - Number of actual trading days
- USA: 252
- Russia: 247-250
- Europe: 250-253
- Crypto (24/7): 365
- Trading Sessions - Define trading hours in format: `hh:mm:ss-hh:mm:ss, hh:mm:ss-hh:mm:ss`
Display
- Show Info Table - Shows calculation parameters in real-time
Market Presets
United States (NYSE/NASDAQ)
Trading Sessions: 09:30:00-16:00:00
Trading Days Per Year: 252
Trading Minutes Per Day: 390
Russia (MOEX)
Trading Sessions: 10:00:00-14:00:00, 14:05:00-18:40:00
Trading Days Per Year: 248
Trading Minutes Per Day: 515
Europe (LSE)
Trading Sessions: 08:00:00-16:30:00
Trading Days Per Year: 252
Trading Minutes Per Day: 510
Germany (XETRA)
Trading Sessions: 09:00:00-17:30:00
Trading Days Per Year: 252
Trading Minutes Per Day: 510
Cryptocurrency (24/7)
Trading Sessions: 00:00:00-23:59:59
Trading Days Per Year: 365
Trading Minutes Per Day: 1440
Use Cases
Options Trading
- Compare HV vs IV - Historical volatility compared to implied volatility helps identify mispriced options
- Volatility mean reversion - Identify when volatility is unusually high or low
- Straddle/strangle selection - Choose optimal strikes based on historical movement
Risk Management
- Position sizing - Adjust position size based on current volatility
- Stop-loss placement - Set stops based on expected price movement
- Portfolio volatility - Monitor individual asset volatility contribution
Market Analysis
- Regime identification - Detect transitions between low and high volatility environments
- Cross-market comparison - Compare volatility across different assets and markets
Why Accurate Trading Hours Matter
Standard HV indicators assume 24-hour trading or use simplified day counts, leading to significant errors in annualized volatility:
- 5-minute chart error : Can be off by 50%+ if using wrong period count
- Options pricing impact : Even 2-3% HV error affects option values substantially
- Intraday vs overnight : Correctly excludes non-trading periods
This indicator ensures your HV calculations match the methodology used in professional options pricing models.
Technical Notes
- Uses actual trading minutes, not calendar days
- Handles multiple clearing periods within a single trading day
- Properly scales volatility across all timeframes
- Logarithmic returns for more accurate volatility measurement
- Compatible with Pine Script v6
Author Notes: This indicator was designed specifically for options traders who need precise volatility measurements across different global markets. The customizable trading sessions ensure your HV calculations align with actual market hours and industry-standard options pricing models.
